Three-Dimensional Similarity in Molecular Docking: Prioritizing Ligand Poses on the Basis of Experimental Binding Modes

Anighoro, Andrew; Bajorath, Jürgen

doi:10.1021/acs.jcim.5b00745

Cited by 44 publications

(48 citation statements)

References 37 publications

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“…Using this approach better enrichments were found, around 10% better (AUC of 0.7 for docking vs 0.8 for 3D similarity) than scoring rankings (Anighoro & Bajorath, 2016).…”

Section: Pose Vs Scoringmentioning

confidence: 99%

Acoplamiento Molecular: Avances Recientes y Retos

2018

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Nota: Artículo recibido el 18 de octubre de 2017 y aceptado el 02 de mayo de 2018. ARTÍCULO DE REVISIÓN abstractAutomated molecular docking aims at predicting the possible interactions between two molecules. This method has proven useful in medicinal chemistry and drug discovery providing atomistic insights into molecular recognition. Over the last 20 years methods for molecular docking have been improved, yielding accurate results on pose prediction. Nonetheless, several aspects of molecular docking need revision due to changes in the paradigm of drug discovery. In the present article, we review the principles, techniques, and algorithms for docking with emphasis on protein-ligand docking for drug discovery. We also discuss current approaches to address major challenges of docking.Key Words: chemoinformatics, computer-aided drug design, drug discovery, structure-activity relationships. Acoplamiento Molecular: Avances Recientes y Retos resuMenEl acoplamiento molecular automatizado tiene como objetivo proponer un modelo de unión entre dos moléculas. Este método ha sido útil en química farmacéutica y en el descubrimiento de nuevos fármacos por medio del entendimiento de las fuerzas de interacción involucradas en el reconocimiento molecular. Durante los últimos 20 años se ha modificado extensamente la técnica de acoplamiento molecular dando resultados precisos en la predicción de los modos de unión. Sin embargo, hay algunas áreas que requieren ser mejoradas substancialmente. En este trabajo se revisan principios, técnicas y algoritmos usados en los programas computacionales del acoplamiento molecular con enfoque en la interacción proteína-ligando aplicado al descubrimiento de nuevos fármacos. También se discuten las estrategias dirigidas a solucionar los principales retos de esta técnica computacional.Palabras Clave: descubrimiento de nuevos fármacos, diseño de fármacos asistido por computadora, quimioinformática, relaciones estructura-actividad.

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“…Using this approach better enrichments were found, around 10% better (AUC of 0.7 for docking vs 0.8 for 3D similarity) than scoring rankings (Anighoro & Bajorath, 2016).…”

Section: Pose Vs Scoringmentioning

confidence: 99%

Acoplamiento Molecular: Avances Recientes y Retos

2018

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“…The aim is to identify the compounds, which interact favorably with the target binding site [32]. Meanwhile, LBVS methods utilize chemical similarity analysis of structurally diverse or known active ligands, with the view of identifying novel small molecules, which could show similar biological activities [33][34][35]. However, both approaches have practical limitations.…”

Section: Computational Approaches In Drug Discoverymentioning

confidence: 99%

Application of Computer Modeling to Drug Discovery: Case Study of PRK1 Kinase Inhibitors as Potential Drugs in Prostate Cancer Treatment

Najjar¹,

Ntie‐Kang²,

Sippl³

2017

Unique Aspects of Anti-Cancer Drug Development

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Computer modeling of natural products (NPs) and NP scaffolds is increasingly gaining importance in drug discovery, particularly in hit/lead discovery programs and at the lead optimization stage. Even though industry had lost interest in the implication of NPs in hit/lead searches, recent reports still show that computer modeling could be a useful assert for the identification of starting scaffolds from nature, which could be further exploited by synthetic modifications. In this chapter, the focus is on some useful tools for computer modeling aimed at the discovery of anticancer drugs from NP scaffolds. We also focus on some recent developments toward the identification of potential anticancer agents by the application of computer modeling. The chapter will lay emphasis on natural sources of anticancer compounds, present some useful databases and computational tools for anticancer drug discovery, and show some recent case studies of the application of computational modeling in anticancer drug discovery, as well as some success stories in virtual screening applications in anticancer drug discovery, highlighting some useful results on the application of on lead discovery (including promising NP scaffolds) against an interesting anticancer drug target, the protein kinase C-related kinase (PRK1).

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“…However, the limited number of high-quality 3D structures of proteins and the structural complexity of natural products (e.g., multiple chiral atoms) restrict the application of current molecular docking and ligand 3D shape similarity approaches. 14 In addition, most of machine learning approaches (except for k-nearest neighbor) require negative samples, while lack of high-quality negative samples further limit the accuracy of current machine learning-based models. 6,15 It is urgently needed to develop new computational approaches for exploring the space of drug targets for natural products at the human proteome.…”

Section: Introductionmentioning

confidence: 99%

Quantitative and Systems Pharmacology. 1.In SilicoPrediction of Drug–Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Fang

Cai

et al. 2017

J. Chem. Inf. Model.

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Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein and kaempherol) with high scores were validated by various literatures. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with the known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram and metformin) with new mechanism-of-action were validated by various published experimental evidences. In summary, this study offers powerful computational systems pharmacology approaches and tools for development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

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Three-Dimensional Similarity in Molecular Docking: Prioritizing Ligand Poses on the Basis of Experimental Binding Modes

Cited by 44 publications

References 37 publications

Acoplamiento Molecular: Avances Recientes y Retos

Acoplamiento Molecular: Avances Recientes y Retos

Application of Computer Modeling to Drug Discovery: Case Study of PRK1 Kinase Inhibitors as Potential Drugs in Prostate Cancer Treatment

Quantitative and Systems Pharmacology. 1.In SilicoPrediction of Drug–Target Interactions of Natural Products Enables New Targeted Cancer Therapy

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